An intelligent model for predicting the day-ahead deregulated market clearing price: A hybrid NN-PSO-GA approach

Document Type : Article


Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran


Under restructuring of electric power industry and changing traditional vertically integrated electric utility structure to competitive, market clearing price (MCP) prediction models are essential for all generation company (GenCos) for their survival under new deregulated environment. In this paper, a hybrid model is presented to predict hourly electricity MCP. The model contains a Neural Network (NN), Particle swarm optimization (PSO) and Genetic Algorithm (GA). The NN is used as the major forecasting module to predict the electricity MCP values and PSO applied to improve the traditional neural network learning capability and optimizing the weights of the NN and GA applied to optimize NN architecture. The main contribution includes: presenting a hybrid intelligent model for MCP prediction; applying K-Means algorithm to clustering NN’s test set and seasonality pattern detection; and evaluation of its performance by improved MAE with penalty factor for positive error. It has been tested on Iranian real-world electricity market for the one month of the year 2010-2013 that result shown average weighted MAE for day ahead MCP prediction is equal to 0.12 and forecasting of MCP can be improved by more than 6.7% and 4%in MAE in compare of simple NN and combination of NN and bat algorithm.


Main Subjects

1. Zaman, F., Elsayed, S.M., Ray, T., and Sarker, R.A. Co-evolutionary approach for strategic bidding in competitive electricity markets", Applied Soft Computing, 51, pp. 1-22 (2017). 2. Lot_, M.M. and Ghaderi, S.F. Possibilistic programming approach for mid-term electric power planning in deregulated markets", International Journal of Electrical Power & Energy Systems, 34, pp. 161-170 (2012). 3. Sandhu, H.S., Fang, L., and Guan, L. Forecasting day-ahead price spikes for the Ontario electricity market". Electric Power Systems Research, 141, pp. 450- 459 (2016). 4. Girish, G.P. Spot electricity price forecasting in Indian electricity market using autoregressive-Garch models", Energy Strategy Reviews, 11-12, pp. 52-57 (2016). 5. Abedinia, O., Amjadi, N., Sha_e-Khah, M., and Catal_ao, J.P.S. Electricity price forecast using combinatorial neural network trained by a new stochastic search method", Energy Conversion and Management, 105, pp. 642-654 (2015). 6. Grilli, L. Deregulated electricity market and auctions: The Italian case", Scienti_c Research an Academic Publisher, 2, pp. 238-242 (2010). 7. Bunn, D.W. Forecasting loads and prices in competitive power markets", IEEE Xplore, 88, pp. 163-169 (2000). 8. Girish, G.P., Rath, B.N., and Akram, V. Spot electricity price discovery in Indian electricity market", Renewable and Sustainable Energy Reviews, 82, pp. 73-79 (2018). 9. Khosravi, A., Nahavandi, S., and Creighton, D. A neural network-GARCH-based method for construction of prediction intervals", Electric Power Systems Research, 96, pp. 185-193 (2013). 10. Janczura, J., Truck, S., Weron, R., and Wol_, R.C. Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling", Energy Economics, 38, pp. 96-110 (2013). 11. Nogales, F.J., Contreras, J., Conejo, A.J., and Espinola, R. Forecasting next-day electricity prices by time series models", IEEE Transactions on Power Systems, 17, pp. 342-348 (2002). 12. Zhang, J., Tan, Z., and Yang, S. Day-ahead electricity price forecasting by a new hybrid method", Computers & Industrial Engineering, 63, pp. 695-701 (2012). 13. Yang, Z., Ce, L., and Lian, L. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods", Applied Energy, 190, pp. 291-305 (2017). 14. Khashei, M., Mokhatab Rafeiei, F., and Bijari, M. Hybrid fuzzy auto-regressive integrated moving average (FARIMAH) model for forecasting the foreign exchange markets", International Journal of Computational Intelligence Systems, 6(5), pp. 954-968 (2013). B. Ostadi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3846{3856 3855 15. Qi, Y., Liu, Y., and Wu, Q. Non-cooperative regulation coordination based on game theory for wind farm clusters during ramping events", Energy, 132, pp. 136- 146 (2017). 16. Lago, J., De Ridder, F., Vrancx, P., and de Schutter, B. Forecasting day-ahead electricity prices in Europe: The importance of considering market integration", Applied Energy, 211, pp. 890-903 (2018). 17. Gholipour Khajeh, M., Maleki, A., Rosen, M.A., and Ahmadi, M.H. Electricity price forecasting using neural networks with an improved iterative training algorithm", International Journal of Ambient Energy, 39, pp. 147-158 (2018). 18. Bento, P.M.R., Pombo, J.A.N., Calado, M.R.A., and Mariano, S.J.P.S. A bat optimized neural network and wavelet transform approach for short-term price forecasting", Applied Energy, 210, pp. 88-97 (2018). 19. Aggarwal, S.K., Saini, L.M., and Kumar, A. Electricity price forecasting in deregulated markets: A review and evaluation", International Journal of Electrical Power & Energy Systems, 31, pp. 13-22 (2009). 20. Singhal, D. and Swarup, K.S. Electricity price forecasting using arti_cial neural networks", International Journal of Electrical Power & Energy Systems, 33, pp. 550-555 (2011). 21. Mandal, P., Senjyu, T., and Funabashi, T. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market", Energy Conversion and Management, 47, pp. 2128- 2142 (2006). 22. Anbazhagan, S. and Kumarappan, N. Day-ahead deregulated electricity market price classi_cation using neural network input featured by DCT", International Journal of Electrical Power & Energy Systems, 37, pp. 103-109 (2012). 23. Youse_, G.R., Kaviri, S.M., Latify, M.A., and Rahmati, I. Electricity industry restructuring in Iran", Energy Policy, 108, pp. 212-226 (2017). 24. Deputy of Power and Energy, Electricity and Energy Planning O_ce of Ministry of Energy, 2015 Energy Balanced Sheet, Tehran, Iran, Ministry of Energy, pp. 165-195 (2015). 25. Baklacioglu, T. Modeling the fuel ow-rate of transport aircraft during ight phases using genetic algorithm-optimized neural networks", Aerospace Science and Technology, 49, pp. 52-62 (2016). 26. Arora, P., Deepali, V., and Varshney, S. Analysis of K-means and K-medoids algorithm for big data", Procedia Computer Science, 78, pp. 507-512 (2016).